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Record W2135752158 · doi:10.1177/0269216310381449

Can the global uptake of palliative care innovations be improved? Insights from a bibliometric analysis of the Edmonton Symptom Assessment System

2010· article· en· W2135752158 on OpenAlexafffundabout
Greta G. Cummings, Patricia Biondo, David Campbell, Carla Stiles, Robin L. Fainsinger, Melanie Muise, Neil A. Hagen

Bibliographic record

VenuePalliative Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPalliative careConstruct (python library)Promotion (chess)MedicineGrey literatureMedical educationNursingMEDLINEComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Clinical research is undertaken to improve care for palliative patients, but little is known about how to support the broad uptake of resultant innovations. The objectives of this paper are to: (1) explore the uptake of the Edmonton Symptom Assessment System throughout the global palliative care community through the lens of a bibliometric review - a research method that maps out the journey of new knowledge uptake by evaluating where key articles are cited in published literature; (2) construct hypotheses on attributes of the global community of palliative care learners; and (3) make inferences on approaches that could improve knowledge transfer. While preliminary, results of the study suggest several specific approaches that could support widespread uptake of innovations in palliative care: targeting publication in high impact, international journals; explicitly focusing on how the innovation is applied to best practice; encouraging additional research to expand on early studies; consciously targeting key professional groups and organizations to promote discussion in the grey literature; and early translation and promotion within multiple languages.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.077
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.412
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2010
Admission routes3
Has abstractyes

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